We are pleased to announce Bioconductor 2.11, consisting of 610
software packages and more than 650 up-to-date annotation packages.
There are 58 new software packages, and many updates and improvements
to existing packages; Bioconductor 2.11 is compatible with R 2.15.1,
and is supported on Linux, 32- and 64-bit Windows, and Mac OS. This
release includes an updated Bioconductor
Amazon Machine Image.
Visit http://bioconductor.org
for details and downloads.

Contents

Getting Started with Bioconductor 2.11

New Software Packages

NEWS from new and existing packages

Packages removed from the release

Getting Started with Bioconductor 2.11

To install Bioconductor 2.11:

Install R 2.15.1. Bioconductor 2.11 has been designed expressly
for this version of R.

New Software Packages

annmap: annmap provides annotation mappings for Affymetrix exon
arrays and coordinate based queries to support deep sequencing data
analysis. Database access is hidden behind the API which provides a
set of functions such as genesInRange(), geneToExon(), exonDetails(),
etc. Functions to plot gene architecture and BAM file data are also
provided. Underlying data are from Ensembl.

AnnotationForge: Provides code for generating Annotation packages and
their databases. Packages produced are intended to be used with
AnnotationDbi.

bigmemoryExtras: This package defines a “BigMatrix” ReferenceClass
which adds safety and convenience features to the
filebacked.big.matrix class from the bigmemory package. BigMatrix
protects against segfaults by monitoring and gracefully restoring the
connection to on-disk data and it also protects against accidental
data modification with a filesystem-based permissions system. We
provide utilities for using BigMatrix-derived classes as assayData
matrices within the Biobase package’s eSet family of classes.
BigMatrix provides some optimizations related to attaching to, and
indexing into, file-backed matrices with dimnames. Additionally, the
package provides a “BigMatrixFactor” class, a file-backed matrix with
factor properties.

bsseq: Tools for analyzing and visualizing bisulfite sequencing data

cancerclass: The classification protocol starts with a feature
selection step and continues with nearest-centroid classification.
The accurarcy of the predictor can be evaluated using training and
test set validation, leave-one-out cross-validation or in a multiple
random validation protocol. Methods for calculation and visualization
of continuous prediction scores allow to balance sensitivity and
specificity and define a cutoff value according to clinical
requirements.

ChIPXpress: ChIPXpress takes as input predicted TF bound genes from
ChIPx data and uses a corresponding database of gene expression
profiles downloaded from NCBI GEO to rank the TF bound targets in
order of which gene is most likely to be functional TF target.

chroGPS: We provide intuitive maps to visualize the association
between genetic elements, with emphasis on epigenetics. The approach
is based on Multi-Dimensional Scaling. We provide several sensible
distance metrics, and adjustment procedures to remove systematic
biases typically observed when merging data obtained under different
technologies or genetic backgrounds.

CNORdt: This add-on to the package CellNOptR handles time-course
data, as opposed to steady state data in CellNOptR. It scales the
simulation step to allow comparison and model fitting for time-course
data. Future versions will optimize delays and strengths for each
edge.

CNORfuzzy: This package is an extension to CellNOptR. It contains
additional functionality needed to simulate and train a prior
knowledge network to experimental data using constrained fuzzy logic
(cFL, rather than Boolean logic as is the case in CellNOptR).
Additionally, this package will contain functions to use for the
compilation of multiple optimization results (either Boolean or cFL).

CNORode: ODE add-on to CellNOptR

CorMut: CorMut provides functions for computing kaks for individual
sites or specific amino acids and detecting correlated mutations
among them. Two methods are provided for detecting correlated
mutations ,including conditional selection pressure and mutual
information. The computation consists of two steps: First, the
positive selection sites are detected; Second, the mutation
correlations are computed among the positive selection sites. Note
that the first step is optional. Meanwhile, CorMut facilitates the
comparison of the correlated mutations between two conditions by the
means of correlated mutation network.

DeconRNASeq: DeconSeq is an R package for deconvolution of
heterogeneous tissues based on mRNA-Seq data. It modeled expression
levels from heterogeneous cell populations in mRNA-Seq as the
weighted average of expression from different constituting cell types
and predicted cell type proportions of single expression profiles.

DirichletMultinomial: Dirichlet-multinomial mixture models can be
used to describe variability in microbial metagenomic data. This
package is an interface to code originally made available by Holmes,
Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as discussed further
in the man page for this package, ?DirichletMultinomial.

DSS: DSS is an R library performing the differential expression
analysis for RNA-seq count data. DSS implements a new dispersion
shrinkage method to estimate the gene-specific biological variance.
Extensive simulation results showed that DSS performs favorabily
compared to DESeq and edgeR when the variation of biological
variances is large.

EasyqpcR: This package is based on the qBase algorithms published by
Hellemans et al. in 2007. The EasyqpcR package allows you to import
easily qPCR data files as described in the vignette. Thereafter, you
can calculate amplification efficiencies, relative quantities and
their standard errors, normalization factors based on the best
reference genes choosen (using the SLqPCR package), and then the
normalized relative quantities, the NRQs scaled to your control and
their standard errors. This package has been created for
low-throughput qPCR data analysis.

flowPeaks: A fast and automatic clustering to classify the cells into
subpopulations based on finding the peaks from the overall density
function generated by K-means.

fmcsR: The fmcsR package introduces an efficient maximum common
substructure (MCS) algorithms combined with a novel matching strategy
that allows for atom and/or bond mismatches in the substructures
shared among two small molecules. The resulting flexible MCSs (FMCSs)
are often larger than strict MCSs, resulting in the identification of
more common features in their source structures, as well as a higher
sensitivity in finding compounds with weak structural similarities.
The fmcsR package provides several utilities to use the FMCS
algorithm for pairwise compound comparisons, structure similarity
searching and clustering.

GeneNetworkBuilder: Appliation for discovering direct or indirect
targets of transcription factors using ChIP-chip or ChIP-seq, and
microarray or RNA-seq gene expression data. Inputting a list of genes
of potential targets of one TF from ChIP-chip or ChIP-seq, and the
gene expression results, GeneNetworkBuilder generates a regulatory
network of the TF.

gmapR: GSNAP and GMAP are a pair of tools to align short-read data
written by Tom Wu. This package provides convenience methods to work
with GMAP and GSNAP from within R. In addition, it provides methods
to tally alignment results on a per-nucleotide basis using the
bam_tally tool.

hapFabia: A package to identify rare and short haplotype clusters in
large sequencing data by FABIA biclustering. Individuals that
inherited a particular DNA segment from the same founder constitute a
haplotype cluster by sharing minor alleles of single nucleotide
variants (SNVs) that tag/mark this segment. Knowledge of haplotype
clusters are relevant for phasing of genotyping data, association
studies, and for population genetics, where they shed light on the
evolutionary history of humans. The package supports VCF formats, is
based on sparse matrix operations, and provides visualization of
haplotype clusters in different formats.

HMMcopy: Corrects GC and mappability biases for readcounts (i.e.
coverage) in non-overlapping windows of fixed length for single whole
genome samples, yielding a rough estimate of copy number for furthur
analysis. Designed for rapid correction of high coverage whole
genome tumour and normal samples.

hpar: A simple interface to and data from the Human Protein Atlas
project.

KEGGprofile: KEGGprofile is an annotation and visualization tool
which integrated the expression profiles and the function annotation
in KEGG pathway maps. The multi-types and multi-groups expression
data can be visualized in one pathway map. KEGGprofile facilitated
more detailed analysis about the specific function changes inner
pathway or temporal correlations in different genes and samples.

methyAnalysis: The methyAnalysis package aims for the DNA methylation
data analysis and visualization. A new class is defined to keep the
chromosome location information together with the data. The current
version of the package mainly focus on analyzing the Illumina
Infinium methylation array data, but most methods can be generalized
to other methylation array or sequencing data.

MiRaGE: The package contains functions for inferece of target gene
regulation by miRNA, based on only target gene expression profile.

MotifDb: More than 2000 annotated position frequency matrices from
five public source, for multiple organisms

motifStack: motifStack is a package that is able to draw amino acid
sequence as easy as to draw DNA/RNA sequence. motifStack provides the
flexibility for users to select the font type and symbol colors.
motifStack is designed for graphical representation of multiple
motifs.

networkBMA: An extension of Bayesian Model Averaging (BMA) for
network construction using time series gene expression data. Includes
assessment functions and sample test data.

NOISeq: Analysis of RNA-seq expression data or other similar kind of
data. Exploratory plots to evualuate saturation, count distribution,
expression per chromosome, type of detected features, features
length, etc. Differential expression between two experimental
conditions with no parametric assumptions.

OrganismDbi: The package enables a simple unified interface to
several annotation packages each of which has its own schema by
taking advantage of the fact that each of these packages implements a
select methods.

OSAT: A sizable genomics study such as microarray often involves the
use of multiple batches (groups) of experiment due to practical
complication. To minimize batch effects, a careful experiment design
should ensure the even distribution of biological groups and
confounding factors across batches. OSAT (Optimal Sample Assignment
Tool) is developed to facilitate the allocation of collected samples
to different batches. With minimum steps, it produces setup that
optimizes the even distribution of samples in groups of biological
interest into different batches, reducing the confounding or
correlation between batches and the biological variables of interest.
It can also optimize the even distribution of confounding factors
across batches. Our tool can handle challenging instances where
incomplete and unbalanced sample collections are involved as well as
ideal balanced RCBD. OSAT provides a number of predefined layout for
some of the most commonly used genomics platform.

PADOG: This package implements a general purpose gene set analysis
method called PADOG that downplays the importance of genes that apear
often accross the sets of genes to be analyzed. The package provides
also a benchmark for gene set analysis methods in terms of
sensitivity and ranking using 24 public datasets from
KEGGdzPathwaysGEO package.

PWMEnrich: Asses the enrichment of already known PWMs (e.g. from
JASPAR) in DNA sequences. Motif hits in a sequence or DNA region are
considered together and P-values derived for their joint pattern. The
package implements multiple algorithms, including fixed-threshold
(Z-score) and threshold-free (Lognormal normalization and Clover)
methods. The main goal is to identify a set of transcription factors
that most likely bind to a single sequence, group of sequences, or
show significantly different binding affinity between two sets of
sequences.

Rcade: Rcade (which stands for “R-based analysis of ChIP-seq And
Differential Expression”) is a tool for integrating ChIP-seq data
with differential expression summary data, through a Bayesian
framework. A key application is in identifing the genes targeted by a
transcription factor of interest - that is, we collect genes that are
associated with a ChIP-seq peak, and differential expression under
some perturbation related to that TF.

ReportingTools: The ReportingTools software package enables users to
easily display reports of analysis results generated from sources
such as microarray and sequencing data. The package allows users to
create HTML pages that may be viewed on a web browser such as Safari,
or in other formats readable by programs such as Excel. Users can
generate tables with sortable and filterable columns, make and
display plots, and link table entries to other data sources such as
NCBI or larger plots within the HTML page. Using the package, users
can also produce a table of contents page to link various reports
together for a particular project that can be viewed in a web
browser.

RGalaxy: Given an R function and its manual page, make the documented
function available in Galaxy.

Risa: The Investigation / Study / Assay (ISA) tab-delimited format is
a general purpose framework with which to collect and communicate
complex metadata (i.e. sample characteristics, technologies used,
type of measurements made) from experiments employing a combination
of technologies, spanning from traditional approaches to
high-throughput techniques. Risa allows to access metadata/data in
ISA-Tab format and build Bioconductor data structures. Currently,
data generated from microarray, flow cytometry and metabolomics-based
(i.e. mass spectrometry) assays are supported. The package is
extendable and efforts are undergoing to support metadata associated
to proteomics assays.

rSFFreader: rSFFreader reads sequence, qualities and clip point
values from sff files generated by Roche 454 and Life Sciences Ion
Torrent sequencers. The plan is to also write out sff files and to
read in flowgrams with some utils

SCAN.UPC: SCAN is a microarray normalization method to facilitate
personalized-medicine workflows. Rather than processing microarray
samples as groups, which can introduce biases and present logistical
challenges, SCAN normalizes each sample individually by modeling and
removing probe- and array-specific background noise using only data
from within each array. (The Universal Probability of expression
Codes (UPC) method is an extension of SCAN and will be added to this
package soon.)

staRank: Detecting all relevant variables from a data set is
challenging, especially when only few samples are available and data
is noisy. Stability ranking provides improved variable rankings of
increased robustness using resampling or subsampling.

synapter: The synapter package provides functionality to reanalyse
label-free proteomics data acquired on a Synapt G2 mass spectrometer.
One or several runs, possibly processed with additional ion mobility
separation to increase identification accuracy can be combined to
other quantitation files to maximise identification and quantitation
accuracy.

TransView: This package provides efficient tools to generate, access
and display read densities of sequencing based data sets such as from
RNA-Seq and ChIP-Seq.

AnnotationForge

extensive overhaul of inparanoid packages means that inparanoid
packages now match to 100 different organisms

Extended support for ensembl mappings to yeast and flies.

NOTEWORTHY CHANGES BETWEEN THIS version and 1.3.11

All chip packages now depend on org packages. This simplifies the
schema and also allows for more convenient updating of these packages
and smaller downloads for users.

chip package mappings that contain probes which map to multiple
targets are now hidden by default, with the ability to be exposed
when required. See the use of the new toggleProbes() method. * * *
1.3.11 SERIES NEWS * * *

aroma.light

Changes in version 1.27.1 (2012-09-12):

ROBUSTNESS: Replaced an .Internal(psort(…)) call in medianPolish()
with a call to matrixStats:::.psortKM().

Changes in version 1.27.0 (2012-08-30):

CLEANUP: Removed weightedMedian(), which has been moved to the
matrixStats package.

BACKWARD COMPATIBILITY: Now package depends on the matrixStats (>=
0.5.2) package, so that weightedMedian() is still available when
loading this package. In future releases, matrixStats will be
downgraded to only be a suggested package.

Changes in version 1.26.1 (2012-08-30):

BUG FIX: robustSmoothSpline() would not work with most recent R devel
versions.

Updated the package dependencies.

Changes in version 1.26.0 (2012-08-19):

Changed the license of aroma.light to GPL (>= 2) from LGPL (>= 2),
because some of the implementation was adopted from GPL (>= 2) code,
i.e. robustSmoothSpline() uses code from stats::smooth.spline().

R CMD check no longer warns about some examples depending on the
R.basic package.

Changes in version 1.25.4 (2012-08-19):

WORKAROUND: Now robustSmoothSpline() robustly locates the proper
native R fit function for smooth splines, which vary with different
releases of R.

Changes in version 1.25.3 (2012-04-16):

Package no longer depends on R.methodsS3, only imports.

Changes in version 1.25.2 (2012-04-16):

‘R CMD check’ no longer complaints about .Internal() calls.

Changes in version 1.25.1 (2012-04-16):

Added support for fitNaiveGenotypes(…, flavor=”fixed”).

GENERALIZATION: Now fitNaiveGenotypes() returns also ‘flavor’ and
‘tau’. The latter are the genotype threshholds used by the caller.

BitSeq

Version: 1.2.0 (27.9.2012)

IMPORTANT change: the way samples-files are passed to getDE,
estimateHyperPar, estimateDE changed. -> instead of providing 2
vectors of filenames for each condition, the files are passed as a
list of vectors, each vector containing filenames for one condition
(allowing use of more than 2 conditions)

new internal structure (not visible to user)

estimateExpression has a new convergence criterion which should
result in producing fewer samples (faster and dropping the use of
MCMC_scaleReduction and MCMC_samplesNmax flags) ->
estimateExpressionLegacy uses the original convergence criterion

library normalization option for getDE, estimateDE,
estimateHyperPar, getMeanVariance (in form of providing the
normalization constants, for getting the constants please use edgeR
or similar)

CAMERA

Changes in version 2012-06-12:

Bugfix in findIsotopes, clears ‘vec’ must be sorted non-decreasingly
error

Changes in version 2012-06-11:

Version 1.13.4

Add ByteCompile: TRUE

Bugfix for findIsotopes, clears subscript out of bound error

Changes in version 2012-05-25:

Version 1.13.2

First changes for improved isotope detection

Changes in version 2012-04-12:

Version 1.13.1

Bugfix for findIsotopes to fix not consecutive isotope label like
[M]+,[M+2]+ without [M+1]+

ChemmineR

Changes in version 2.10.0:

NEW FEATURES

Streaming functionality for SDFs enables processing of millions of
molecules on a laptop

clusterProfiler

bug fixed of buildGOmap due to the empty GO annotation query from
biomaRt <2012-07-18, Wed>

Changes in version 1.5.0:

bump up version to 1.5.0 for BioC 2.11 devel

add URL in DESCRIPTION

update citation and add biocView GeneSetEnrichment <2012-05-09, Wed>

support zebrafish <2012-05-18, Mon>

cqn

Changes in version 1.3:

Bugfix to the vignette; the two-panel (color) plot on page 6 used CQN
corrected data as blue points in both panels. Now the left plot
shows standard RPKM in blue. Thanks to Maria Keays <email:
mkeays@ebi.ac.uk>.

Small fix to the vignette in the edgeR example, caused by changes to
edgeR.

Updated the citations in the vignette and the CITATION file.

cqn.fixedlength has been removed, using cqn(lengthMethod = “fixed”)
instead.

A call slot has been added to cqn objects.

cqn() now accepts count matrices with 1 column or vectors (although
it makes little sense to use the function on such data).

Added Questions and Answers to the vignette and moved vignette to
vignettes dir.

cummeRbund

v1.99.6
Notes:
- ‘annotation’ and “annotation<-“ generics were moved to
BiocGenerics 0.3.2. Now using appropriate generic function,
but requiring BiocGenerics >= 0.3.2
v1.99.5
Bugfixes:
- Added replicates argument to csDistHeat to view distances
between individual replicate samples.
- Appropriately distinguish now between ‘annotation’ (external
attributes) and features (gene-level sub-features).
- csHeatmap now has ‘method’ argument to pass function for any
dissimilarity metric you desire. You must pass a function
that returns a ‘dist’ object applied to rows of a
matrix. Default is still JS-distance.

v1.99.3
New Features:
- Added diffTable() method to return a table of differential
results broken out by pairwise comparison. (more
human-readable)
- Added sigMatrix() method to CuffSet objects to draw heatmap
showing number of significant genes by pairwise comparison
at a given FDR.
- A call to fpkm() now emits calculated (model-derived)
standard deviation field as well.
- Can now pass a GTF file as argument to readCufflinks() to
integrate transcript model information into database backend
* Added requirement for rtracklayer and GenomicFeatures
packages.
* You must also indicate which genome build the .gtf was
created against by using the ‘genome’ argument to
readCufflinks.
- Integration with Gviz:
* CuffGene objects now have a makeGeneRegionTrack()
argument to create a GeneRegionTrack() from transcript
model information
* Can also make GRanges object
* ONLY WORKS IF YOU READ .gtf FILE IN WITH readCufflinks()
- Added csScatterMatrix() and csVolcanoMatrix() method to
CuffData objects.
- Added fpkmSCVPlot() as a CuffData method to visualize
replicate-level coefficient of variation across fpkm range
per condition.
- Added PCAplot() and MDSplot() for dimensionality reduction
visualizations (Principle components, and multi-dimensional
scaling respectively)
- Added csDistHeat() to create a heatmap of JS-distances
between conditions.

Bugfixes:
- Fixed diffData ‘features’ argument so that it now does what
it’s supposed to do.
- added DB() with signature(object=”CuffSet”) to NAMESPACE

Notes:
- Once again, there have been modifications to the underlying
database schema so you will have to re-run
readCufflinks(rebuild=T) to re-analyze existing datasets.
- Importing ‘defaults’ from plyr instead of requiring entire
package (keeps namespace cleaner).
- Set pseudocount=0.0 as default for csDensity() and
csScatter() methods (This prevents a visual bias for genes
with FPKM <1 and ggplot2 handles removing true zero values).

v1.99.2
Bugfixes:
- Fixed bug in replicate table that did not apply
make.db.names to match samples table.
- Fixed bug for missing values in *.count_tracking files.
- Now correctly applying make.db.names to
*.read_group_tracking files.
- Now correctly allows for empty *.count_tracking and
*.read_group_tracking files
v1.99.1
- This represents a major set of improvements and feature
additions to cummeRbund.
- cummeRbund now incorporates additional information emitted from
cuffdiff 2.0 including:
- run parameters and information.
- sample-level information such as mass and scaling factors.
- individual replicate fpkms and associated statistics for all
features.
- raw and normalized count tables and associated statistics
all features.

New Features:
- Please see updated vignette for overview of new features.
- New dispersionPlot() to visualize model fit (mean count vs
dispersion) at all feature levels.
- New runInfo() method returns cuffdiff run parameters.
- New replicates() method returns a data.frame of
replicate-level parameters and information.
- getGene() and getGenes() can now take a list of any
tracking_id or gene_short_name (not just gene_ids) to
retrieve a gene or geneset.
- Added getFeatures() method to retrieve a CuffFeatureSet
independent of gene-level attributes. This is ideal for
looking at sets of features outside of the context of all
other gene-related information (i.e. facilitates
feature-level analysis)
- Replicate-level fpkm data now available.
- Condition-level raw and normalized count data now available.
- repFpkm(), repFpkmMatrix, count(), and countMatrix are new
accessor methods to CuffData, CuffFeatureSet, and
CuffFeature objects.
- All relevant plots now have a logical ‘replicates’ argument
(default = F) that when set to TRUE will expose replicate
FPKM values in appropriate ways.
- MAPlot() now has ‘useCount’ argument to draw MA plots using
count data as opposed to fpkm estimates.

Notes:
- Changed default csHeatmap colorscheme to the much more
pleasing ‘lightyellow’ to ‘darkred’ through ‘orange’.
- SQLite journaling is no longer disabled by default (The
benefits outweigh the moderate reduction in load times).

Bugfixes:
- Numerous random bug fixes to improve consistency and improve
performance for large datasets.
v1.2.1
Bugfixes:
-Fixed bug in CuffFeatureSet::expressionBarplot to make
compatible with ggplot2 v0.9.
New Features:
- Added ‘distThresh’ argument to findSimilar. This allows you
to retrieve all similar genes within a given JS distance as
specified by distThresh.
- Added ‘returnGeneSet’ argument to findSimilar. [default =
T] If true, findSimilar returns a CuffGeneSet of genes
matching criteria (default). If false, a rank-ordered data
frame of JS distance values is returned.
- findSimilar can now take a ‘sampleIdList’ argument. This
should be a vector of sample names across which the distance
between genes should be evaluated. This should be a subset
of the output of samples(genes(cuff)).
Notes:
- Added requirement for ‘fastcluster’ package. There is very
little footprint, and it makes a significant improvement in
speed for the clustering analyses.
DART
—-

Added ability to generate sets of consensus peaksets based on
metadata attributes: for example create consensus peaksets for each
tissue type and/or condition, or for all unique samples by taking the
consensus of their replicate peaksets

Read counting (dba.count)

Compute Signal-to-Noise ratio when counting

Added bScaleControl to down-scale control reads by default

Add option to specify a mask in peak parameter to limit which
peaksets are used to for a consensus by overlap. Works with new
consensus peakset options in dba.peakset

DSS

Fixed a bug in newSeqCountSet in dealing with the input experimental
designs.

Fixed a bug in computing local FDR.

Add options to model the relationship between dispersion and mean
expression.

Changes in version 0.99:

NEW FEATURES

Initial release.

EasyqpcR

Changes in version 1.0.0:

First version of this package.

easyRNASeq

Changes in version 1.3.14:

NEW FEATURES

easyRNASeq now returns a SummarizedExperiment in an effort to
consolidate the objects used for Next Generation Sequencing in
Bioconductor. This is the new default of the count function. The
count function is a new function to supersed easyRNASeq in the coming
development version (1.5.x) to consolidate the parameters and output
of the easyRNASeq function.

BUG FIXES

corrected a validity check that went permissive.

changed the print method to display the read length range when
dealing with variable read lengths rather than every single value.

Changes in version 1.3.13:

BUG FIXES

Same correction as in the stable version 1.2.5, but for those already
corrected in version 1.3.3.

Changes in version 1.3.12:

Providing the ‘outputFormat’ argument is not necessary anymore, it
defaults to matrix (i.e. count table).

Relaxed the gtf file checking. If the gene_name is absent, the
gene_id is used instead.

Improved some reporting and remove a bottle-neck occuring when there
are many sequences in the reference. BUG FIXES

Ensure that only the matched ranges are returned when reading gapped
alignments.

The library size is more exactly calculated and is the number of
aligned reads.

Corrected a bug in the validity checking that prevented bam files
created by different aligners using the same reference to be
processed as the reference sequences were not ordered in the same
fashion.

Changes in version 1.3.11:

BUG FIXES

Fixed a bug in the gtf file handling reported by Mark Robinson.

Changes in version 1.3.10:

Some vignette discrepancies have been corrected. Thanks to Richard
Friedman for spotting them.

Providing the ‘filesDirectory’ argument is not necessary anymore, if
the files to proceed are present in the current directory. Indeed,
this parameter now defaults to the current directory as can be found
out using ‘getwd()’. BUG FIXES

Fixed a bug introduced by a change in the IRanges coverage function
return value.

Changes in version 1.3.9:

Added the manuscript citation.

Updated the package version dependencies. BUG FIXES

Improved the support for reads of different lengths.

A cosmetic change to report read lengths as well when read files with
variable read length are processed.

Corrected a bug and enhanced the loading of gtf annotation files.
Thanks to Tomasz Kulinski for spotting the issue and providing the
dataset to reproduce it.

Changes in version 1.3.8:

NEW FEATURES

Now bam files can be processed in parallel (long time request from
Wade Davis). If the easyRNASeq argument ‘nbCore’ is greater than 1 (1
being the default), then that many core will be used to process the
read files in parallel. Pay attention not to use too many cores and
have enough memory available. The memory load scales up linearly with
the number of files processed.

Changes in version 1.3.7:

NEW FEATURES

easyRNASeq now supports read of different lenghts. Thanks to Mark
Robinson for the toy dataset.

Added a function that lists existing organism conversion when
applying the validity checks.

Added a bp.coverage to the fetchCoverage function that defaults to
FALSE. To allow for variable length reads, it now returns read
coverage proportion per bp by default.

Added additional checks in the .checkArguments internal function.

BUG FIXES

Not a real bug, but more a consolidation. When an organism is unknown
and no custom.map is provided, then the validity checks are turned
off and a warning is emitted.

Providing the chr.sizes as as list has been deprecated. Only named
numeric vector are supported.

Removed a now useless warning in the .readGffGtf function.

Modified the RPKM function generic to avoid using a ‘protected’ word
as argument: i.e. ‘unique’ was replaced by ‘simplify’

Changes in version 1.3.6:

NEW FEATURES

It is now possible to pass arguments to list files through the three
dots. I.e. setting recursive=TRUE is now possible.

BUG FIXES

Corrected a bug in the .getArguments internal function.

Changes in version 1.3.5:

NEW FEATURES

bam is now the default format for the easyRNASeq method.

chromosome sizes are now extracted from the BAM header when the
‘chr.sizes’ argument is set to “auto”. Thanks to Simon Anders for
pushing that off my TODO list and the nice implementation.

BUG FIXES

Adapted to an API change of the edgeR package for estimating the
tagwise dispersion.

Changes in version 1.3.4:

NEW FEATURES

Added an additional validity check for chromosome names Thanks to
Simon Anders for generating a reproducible use-case for that. Same
change as in the stable version 1.2.3

Ensure that gtf with non Ensembl ID are correctly parsed as well.

Changes in version 1.3.3:

Converted the package to use Roxygen2, a Doxygen like in-source
documentation system for generating the RD and NAMESPACE. The
original man page were converted using the Rd2roxygen package and the
resulting in-source documentation manually edited. NEW FEATURES

Added a type accessor for Genome_intervals object

Added a coercion to GRangesList from Genome_intervals object BUG
FIXES

Adapted to the new arguments of the edgeR estimateTagwiseDisp
function

Removed the dispersion.method argument from the plotMeanVar edgeR
method call as this argument is defunct.

Changes in version 1.3.2:

BUG FIXES

Corrected a bug that was considering a GTF file as a GFF file. Thanks
to Simon Anders for spotting this.

Changes in version 1.3.1:

NEW FEATURES

Added an enhanced read length check (same as stable 1.2.1 change)

Changes in version 1.3.0:

New development version for Bioconductor 2.11

Changes in version 1.2.5:

BUG FIXES

Corrected a bug in the condition file name checking.

When using edgeR, it was not possible to de-activate the drawing of
the quality assessment plots.

Some edgeR changes to the API have been ported to the stable R
version, should not have occured… The following are changes that
adapt to that new API, changes ported from version the easyRNASeq
development version 1.3.3…

Adapted to the new arguments of the edgeR estimateTagwiseDisp
function

Removed the dispersion.method argument from the plotMeanVar edgeR
method call as this argument is defunct.

Changes in version 1.2.4:

Added the manuscript citation.

Updated the package version dependencies.

Changes in version 1.2.3:

NEW FEATURES

Added an additional validity check for chromosome names Thanks to
Simon Anders for generating a reproducible use-case for that.

Ensure that gtf with non Ensembl ID are correctly parsed as well.

Changes in version 1.2.2:

BUG FIXES

Corrected a bug that was considering a GTF file as a GFF file. Thanks
to Simon Anders for spotting this.

Changes in version 1.2.1:

NEW FEATURES

Added an enhanced read length check

EBImage

Changes in version 4.0.0:

NEW FEATURES

‘transpose’ function for transposing an image by swapping its spatial
dimensions

greyscale functions for computation of the self-complementary top-hat
(I. Kats)

a median filter based on Perreault’s constant time median filter (J.
Barry)

SIGNIFICANT USER-VISIBLE CHANGES

removed all dependencies towards GTK+ and ImageMagick

replaced the former GTK+ based ‘display’ function by a new one
displaying images using either a JavaScript image viewer, or R’s
built-in raster graphics

‘readImage’ and ‘writeImage’ now rely on ‘jpeg’, ‘png’ and ‘tiff’
packages and do not depend on ImageMagick any more

added support for images containing an alpha channel; both greyscale
and color images with an alpha channel are stored as a ‘colormode =
Color’ Image

refactored the functions, not using ImageMagick any longer:
‘translate’, ‘affine’, ‘rotate’, ‘resize’

EDASeq

Added a color_code option and changed the behavior of col in
biasPlot.

Updated CITATION file.

Added an option to withinLaneNormalization and
betweenLaneNormalization to return unrounded values.

A new way to deal with zero counts by adding a small positive
constant.

edgeR

Changes in version 3.0.0:

New chapter in the User’s Guide covering a number of common types of
experimental designs, including multiple groups, multiple factors and
additive models. Many other updates to the User’s Guide and to the
help pages.

New function edgeRUsersGuide() to open the User’s Guide in a pdf
viewer.

Many functions have made faster by rewriting the core computations in
C++. This includes adjustedProfileLik(), mglmLevenberg(),
maximizeInterpolant() and goodTuring().

New argument verbose for estimateCommonDisp() and
estimateGLMCommonDisp().

The trended dispersion methods based on binning and interpolation
have been rewritten to give more stable results when the number of
genes is not large.

The amount by which the tagwise dispersion estimates are squeezed
towards the global value is now specified in estimateTagwiseDisp(),
estimateGLMTagwiseDisp() and dispCoxReidInterpolateTagwise() by
specifying the prior degrees of freedom prior.df instead of the prior
number of samples prior.n.

The weighted likelihood empirical Bayes code has been simplified or
developed in a number of ways. The old functions weightedComLik() and
weightedComLikMA() are now removed as no longer required.

The functions estimateSmoothing() and approx.expected.info() have
been removed as no longer recommended.

The span used by estimateGLMTagwiseDisp() is now chosen by default as
a decreasing function of the number of tags in the dataset.

New method “loess” for the trend argument of estimateTagwiseDisp,
with “tricube” now treated as a synonym.

New functions loessByCol() and locfitByCol() for smoothing columns of
matrix by non-robust loess curves. These functions are used in the
weighted likelihood empirical Bayes procedures to compute local
common likelihood.

glmFit now shrinks the estimated fold-changes towards zero. The
default shrinkage is as for exactTest().

predFC output is now on the natural log scale instead of log2.

mglmLevenberg() is now the default glm fitting algorithm, avoiding
the occasional errors that occurred previously with mglmLS().

The arguments of glmLRT() and glmQLFTest() have been simplified so
that the argument y, previously the first argument of glmLRT, is no
longer required.

glmQLFTest() now ensures that no p-value is smaller than what would
be obtained by treating the likelihood ratio test statistic as
chisquare.

glmQLFTest() now treats tags with all zero counts in replicate arrays
as having zero residual df.

gof() now optionally produces a qq-plot of the genewise goodness of
fit statistics.

Argument null.hypothesis removed from equalizeLibSizes().

DGEList now longer outputs a component called all.zeros.

goodTuring() now longer produces a plot. Instead there is a new
function goodTuringPlot() for plotting log-probability versus
log-frequency. goodTuring() has a new argument ‘conf’ giving the
confidence factor for the linear regression approximation.

Added plot.it argument to maPlot().

ExiMiR

Changes in version 1.99.1:

NAMESPACE: removing the load of limma and affy that are in
dependencies

Changes in version 1.99.0:

inst/doc/fig0.png: new figure

man/bg.correct.miR.Rd: new help file

man/createAB.Rd: new help file

man/NormiR.methods.Rd: new help file

R/createAB.R: new feature implementation

R/NormiR.methods: new file for giving availables methods

All the others files have been updated according new features
implementation

fabia

Changes in version 2.3.1:

NEW FEATURES

Getters and setters for class Factorization

flowCore

add new classes “filters”, “filtersList” to allow flowViz to plot
multiple filters/gates for one flowFrame

add argument “emptyValue” to read.FCS API so that parser can still
work correctly when either cases below occurs :

there is double-delimiter in keyword values (sometime
like\n\\c:\\path\\…)

there is empty keyword
value\n(\\keyword1\\value1\\keyword2\\\\keyword3)

fix the bug that malformed spillover matrix in write.FCS

flowViz

CHANGES IN VERSION 1.21.1

1.add modified lattice theme to flowViz and change the default color
scheme for non-smoothed xyplot
2.add stat=TRUE to display population % in xyplot and add abs=FALSE
and pos=0.5 to control the position of gate labels
3.made change to prepanel.xyplot.flowset so that it return an empty
list instead of NULL value for empty panels.This was causing the
error thrown by lattice:::limits.and.aspect which calculates the
scales for each panel and expects non-null return value from
prepanel function
4.remove the old src and R code for hexbin and add hexbin package
based hexagon plot support within panel.xyplot.flowframe
5.add binTrans argument to xyplot that gets passed to hexin to
transform the raw counts. sqrt is the default,NULL value means no
transformation.
6.add new classes “filters”, “filtersList” to allow flowViz to plot
multiple filters/gates for one flowFrame

fmcsR

Changes in version 0.99.0:

fmcsR submitted to Bioconductor

FunciSNP

Changes in version 0.99.0 (2012-05-25):

Updated package to address issues and comments by BioC curator.

Changes in version 0.2.0 (2012-05-18):

Initial Bioconductor release. New citation included.

Changes in version 0.1.0 (2012-02-12):

Created. Initial build and release of FunciSNP.

GeneAnswers

Changes in version 1.14:

NEW FEATURES

Multigroup concept-gene analysis html report supports interactive
network with cytoscape web support as well as original fixed images

caBIO pathway and REACTOME pathway are included with xml queries,
therefore, internet access is required.

The total number of pooled genes in Hypergeometric test can be set by
the amount of genes in annotation library or the total annoted genes
in the given species.

mcols() is now the preferred way (over elementMetadata() or values())
to access the metadata columns of a GenomicRanges, GRangesList,
GappedAlignments, GappedAlignmentPairs, SummarizedExperiment object,
or any Vector object. elementMetadata() and values() might go away at
some point in the (not so close) future.

Add “$” and “$<-“ methods for GenomicRanges only. Provided as a
convenience and as the result of strong popular demand. Note that
those methods are not consistent with the other “$” and “$<-“ methods
in the IRanges/GenomicRanges infrastructure, and might confuse some
users by making them believe that a GenomicRanges object can be
manipulated as a data.frame-like object. It is therefore recommended
to use them only interactively, and their use in scripts or packages
is discouraged. For the latter, use ‘mcols(x)$name’ instead of
‘x$name’.

No more warning when doing as(x, “GRanges”) on a RangedData object
with no “strand” column.

Refactor “[” method for GenomicRanges objects. The new implementation
always preserves the names of the selected elements instead of trying
to return a GenomicRanges object with unique names. This new behavior
is consistent with subsetting of ordinary vectors and other Vector
objects defined in IRanges/GenomicRanges. Also modify “seqselect”
method for GenomicRanges objects so it also preserves the names of
the selected elements (and thus remains consistent with new behavior
of “[” method for GenomicRanges objects).

No more names on the integer vector returned by “ngap” method for
GappedAlignments objects.

Fix several issues with “precede”, “follow”, “nearest”, and
“distance” methods for GenomicRanges objects.

Fix bug in summarizeOverlaps(…, ignore.strand=TRUE).

6x speedup (and a 6x memory footprint reduction) or more when using
encodeOverlaps() on big GRangesList objects.

Fix bug in renameSeqlevels() wrt order of rename vector.

Fix bug in selectEncodingWithCompatibleStrand().

genoset

1.9.8 GRanges everywhere! GenoSet now supports GRanges in the locData
slot. All functions that take RangedData now also take GRanges. I
have unified the API for GRanges, RangedData, and GenoSet to the point
that GenoSet classes and the functions in the package are agnostic to
the type of range object. I have not, however, fixed the contentious
issue of using the “$” operator with GRanges to access
elementMetadata.

1.9.10 Subsetting by location now only with GRanges and RangedData.
Dropped RangesList to avoid weird errors about the
RangedDataOrRangesListOrGRanges class union. Apparently the
RangedDataOrGRanges class union is fine. I think RangesLists are not
used often anyway.

GGtools

Changes in version 4.6:

The primary tools for one-population analyses are best.cis.eQTLs and
transScores. Multipopulation analyses are handled with
meta.best.cis.eQTLs and meta.transScores.

High volume genotype data has been addressed by packaging
ExpressionSet and chromosome-specific SnpMatrix instances; requests
for expression plus genotype data are directed to packages mediated
through GGBase::getSS.

Two species of data filtering parameters that may be used jointly in
the primary tools are exFilter, which operates on expression
component prior to any analyses, and smFilter, which operates on the
entire smlSet. exFilter may be used to isolate samples of interest
early in the workflow, for example when an expression plus genotype
package includes samples from distinct tissues on the same
individuals.

export termSim, which can be used in other ontological semantic
similarity measurement <2012-06-14, Thu>

update vignette. <2012-06-14, Thu>

GSEABase

Changes in version 1.19:

NEW FEATURES

Added UniprotIdentifier class

Gviz

Changes in version 1.2.0:

NEW FEATURES

A SequenceTrack class has been added to draw genomic sequence
information on a Gviz plot. Possible inputs for the track are
DNAStringSet objects or directly from BSgenome packages.

GeneRegionTracks can now deal with coding and non-coding regions by
means of the feature property in combination with the thinBoxFeature
display parameter.

StackedTracks now have a new display parameter ‘reverseStacking’
which reverts the horizontal ordering of stacked items. If set to
TRUE, the lowest items are moved to the top of the stack, and vice
versa.

SIGNIFICANT USER-VISIBLE CHANGES

Updated the show methods for most tracks to give more meaningful and
more compact information about the track’s content. Availablability
of data on other chromosomes than the currently active one should now
be indicated.

IdeogramTracks can now be constructed from a cytoband table via the
new bands argument in the constructor.

AnnotationTrack objects now by default draw connecting lines in a
light gray color. This feature can be controlled via the col.line
display parameter.

Sliding window summarization can now deal with NA values.

Exporting drawGD from the name space now to allow for sub-classing of
GdObjects in other packages.

When building GeneRegionTracks from TrasncriptDb objects, the
information about UTRs and coding regions is now retained.

BUG FIXES

When zooming into the emty space between two grouped features, the
connecting line will now be plotted for all classes inheriting from
AnnotationTrack.

An error in calculating ylims when drawing AlignedReadTracks has been
fixed.

Numerous other little fixes that mainly aim at improving performance.

GWASTools

Changes in version 1.3.16:

Added convertVcfGds to extract bi-allelic SNPs from a VCF file.

Added ncdfImputedDosage to convert output from common imputation
programs to NetCDF. assocTestRegression has an additional argument
dosage=TRUE to be used with these files.

Added vignette describing GWASTools data structures.

Changes in version 1.3.15:

Bug fix in pedigreePairwiseRelatedness related to use of character
identifiers.

Changes in version 1.3.14:

assocTestRegression returns NA for snps where cases or controls are
monomorphic, added assocTestFisherExact to use in that case.

htSeqTools

iPAC

Two beta methods available to reconcile data between the COSMIC and
PDB databases.

IRanges

Changes in version 1.16.0:

NEW FEATURES

as( , “SimpleList”), as( , “CompressedList”), and as( , “List”) now
work on atomic vectors, and each element of the vector corresponds to
an element of the returned List (this is consistent with as.list).

Add as.list,Rle method.

Add as.matrix,Views method. Each view corresponds to a row in the
returned matrix. Rows corresponding to views shorter than the longest
view are right-padded with NAs.

Add FilterClosure closure class for functions placed into a
FilterRules. Has methods for getting parameters and showing.

Support ‘na.rm’ argument in “runsum”, “runwtsum”, “runq”, and
“runmean” methods for Rle and RleList objects.

Add splitAsList() and splitAsListReturnedClass().

Improve summary,FilterRules to support serial evaluation, discarded
counts (instead of passed) and percentages.

Make rename work on ordinary vector (in addition to Vector).

Add coercion from RangedData to CompressedIRangesList, IRangesList,
or RangesList. It propagates the data columns (aka values) of the
RangedData object to the inner metadata columns of the RangesList
object.

Enhance “[” methods for IRanges, XVector, XVectorList, and
MaskCollection objects, as well as “[<-“ method for IRanges objects,
by supporting the following subscript types: NULL, Rle, numeric,
logical, character, and factor. (All the methods listed above already
supported some of those types but no method supported them all).

Add remapHits() for remapping the query and subject hits of a Hits
object.

Add expand() for expanding a DataFrame based on the contents of one
or more designated columms.

After being deprecated (in BioC 2.9) and defunct (in BioC 2.10), the
“as.vector” method for AtomicList objects is back, but now it mimics
what as.vector() does on an ordinary list i.e. it’s equivalent to
‘as.vector(as.list(x), mode=mode)’. Also coercions from AtomicList to
logical/integer/numeric/double/complex/character/raw are back and
based on the “as.vector” method for AtomicList objects i.e. they work
only on objects with top-level elements of length <= 1.

DataFrame constructor now supports ‘check.names’ argument.

Add revElements() generic with methods for List and CompressedList
objects.

SIGNIFICANT USER-VISIBLE CHANGES

Splitting / relisting a Hits object now returns a HitsList instead of
an ordinary list.

Operations in the Ops group between a List and an atomic vector
operand now coerce the atomic vector to List (SimpleList or
CompressedList) before performing the operation. Also, operands are
recycled and a better job is done returning zero length results of
the correct type.

Change the warning for ‘Integer overflow …’ thrown by sum() on
integer-Rle’s

union,Hits method now sorts the returned hits first by query hit,
then by subject hit.

Add mcols() accessor as the preferred way (over elementMetadata() and
values()) to access the metadata columns of a Vector object.

By default, mcols(x) and elementMetadata(x) do NOT propagate the
names of x as the row names of the returned DataTable anymore.
However the user can still get the old behavior by doing mcols(x,
use.names=TRUE).

[<-,XVectorList now preserves the original names instead of
propagating the names of the replacement value, which is consistent
with how [<- operates on an ordinary vector/list.

coverage() now returns a numeric-Rle when passed numeric weights.

When called on a List object with use.names=TRUE, unlist() no longer
tries to mimic the kind of non-sense name mangling that
base::unlist() does (e.g. on list(a=1:3)) in a pointless effort to
return a vector with unique names.

Fix a bunch of subsetting methods that were not subsetting the
metadata columns: “[”, “subseq”, and “seqselect” methods for XVector
objects, “seqselect” and “window” methods for XVectorList objects,
and “[” method for MaskCollection objects.

Fix empty replacement with [<-,Vector

Make %in% robust on an empty ‘table’ argument when operating on Hits
objects.

iSeq

Changes in version 1.9.0:

Previously, peakreg fails when only one posterior probability is
greater than ppcut or fdrcut, although this condition is really rare.
This bug has been fixed.

A tutorial of ChIP-seq data analysis using iSeq and an R script
called iSeq.R that can be used as a command line program have been
posted at
https://sites.google.com/site/quincymobio/teaching-materials

limma

Many updates to the User’s Guide. Sections have been added on
reading single channel Agilent and Illumina data. The chapter on
experimental designs has been split into three chapters on
single-channel, common reference and two-color designs respectively.
The material on the fixed effect approach to technical replication
has been deleted. There are new sections on nested interactions for
factorial designs and on multi-level designs.

The links to the Apoa1, Weaver and Bob1 datasets in the User’s Guide
have been updated to help users download the data themselves if they
wish to repeat the case study analyses.

The help page for camera() now cites published paper Wu and Smyth
(NAR, 2012). In view of the results of this paper, the claim is no
longer made on help page for geneSetTest() that genes might be
treated as independent when the experimental units are genetically
identical mice.

Minor edits to CITATION file.

New function propTrueNull() for fast estimation of the proportion of
true null hypotheses from a vector of p-values.

New function zscore() to compute z-score equivalents for deviates
from any continuous distribution. Includes the functionality of the
older functions zscoreGamma() and zscoreT() as special cases.

roast() now accepts observation level weights, through a new argument
‘weights’.

loessFit() now applies minimum and maximum bounds by default to avoid
zero or infinite weights. Equal weights are now treated as if the
weights were NULL, even all zero weights, so that the lowess code is
called instead of the loess code.

When there are no weights, loessFit() now extracts residuals directly
from the C code output instead of computing in R.

fitFDist() now permits missing values for x or zero values for df1
even when there is a covariate. This means that squeezeVar() and
eBayes() now work with trends even when not all the data values are
informative.

New argument ‘file’ for convest(), implementing edits contributed by
Marcus Davy. Arguments doplot and dereport renamed to ‘plot’ and
‘report’.

Two improvements for plotMDS(). It now coerces labels to be
character, and now makes extra room on the plot when the text labels
are wide.

plotMDS() no longer gives an error when the requested number of top
genes is greater than the total number of rows of data.

Code speed-up for alias2SymbolTable()

any(duplicated()) replaced by anyDuplicated() in several functions.

Fix to voom() so that it computes weights correctly even when the
design matrix is not of full rank.

Bug fix for roast() when the fitted model has only one coefficient.

lmdme

Changes in version 0.99.1:

MINOR CHANGES

Getters now accept a character vector in term parameter, in order
to specify one than one term if required. In addition, design and
model were added, and pvalues like Fvalues were changed to
match slot names. (Thanks to Valerie Obenchain)

lmdme now works with NA presence in data matrix. This bug is
related to lmFit intercept coefficient behavior, which breaks the
data structure using drop (to numeric instead of keeping a matrix
with one column) only if NA are present.

lmdme is now the only constructor. Method initialize was erased
due to different reasons as described in
https://stat.ethz.ch/pipermail/bioc­devel/2012­August/003554.html

decomposition example sections using subset parameter for
simplicity (not all the data has to be decomposed in the example).

Enhance of biplot and screeplot functions with term and mfcol
to simplify the graphic output specification.

DOCUMENTATION

NEWS file was added.

minfi

Changes in version 1.3:

Updated preprocessSwan to fix a bug when mSet was not set to the
default value of NULL. Specifically, now the “counts” tables is used
to construct “subset”.

Changed the function manifestNew() to IlluminaMethylationManifest().

Added IlluminaMethylationAnnotation().

Added placeholders for unit testing based on RUnit.

Introduced a new show method for MethylSet and RGChannelSet, derived
from the eSet method in Biobase.

The annotation slot of a MethylSet/RGChannelSet is now intended to
not be a scalar, but instead have length 2 with components ‘array’
and ‘annotation’. This foreshadows introdution of annotation
packages for use with minfi.

Reorganization of R files; rewriting of the man pages for MethylSet,
RGChannelSet.

getMeth, getUnmeth, getBeta, getM are now methods.

bug fix to qcReport thanks to Tao Shi.

Changes to getBeta / getM, both in terms of which arguments the
methods take and how the values are computed.

Changes to the manifest structure; it now has separate slots for
genotype probes and these probes are no longer part of a MethylSet
(using eg. preprocessRaw). They can be accessed using
getProbeInfo(rgSet, type) with type equal to “SnpI” or “SnpII”.

Introduction of mapToGenome, getLocations and the new class
GenomicMethylSet. man pages are reasonably complete, still need to
add examples to the vignette. This will be a standard part of an
extended pipeline.

Introduction of IlluminaHumanMethylation450lannotation.ilmn.v1.2
which contains some new annotation needed for
mapToGenome/getLocations. This package will be split into several
packages moving forward, in an attempt to harmonize efforts by us and
Tim Triche. getLocations/mapToGenome will stay the same.

getControlTypes added (returns the different types of control
probes).

GenomicMethylSet now inherits a number of methods including
granges(), start(), end() etc. from SummarizedExperiemnt. They have
therefore been deleted from minfi.

Bugfix to getLocations(…, mergeManifest = TRUE). It now longer
throws an error.

mapToGenome now returns a GenomicMethylSet ordered according to the
chromosome name ordering chr1,..,chr22,chrX,chrY,unmapped, the last
one not present if drop=TRUE (default).

MLInterfaces

Changes in version 1.37.1:

added NEWS file (lgatto) <2012-04-02 Mon>

renamed ‘predScores’ to ‘predScore’ and new ‘predScores’ method that
returns the full prediction score matrix (lgatto) <2012-04-02 Mon>

caching full header in level 1; this is required when and MSnExp
instance with many spectra (created from many raw files) is
quantified - calling header(object) is a too big overhead compared to
actual reporter quantification. <2012-04-20 Fri>

The header() method now uses the cached dataframe if level >= 1; the
(unexported) .header function can be used to generate the dataframe
using the assayData slot data. <2012-04-20 Fri>

Setting processingData in MSnSet initialisation. <2012-04-20 Fri>

Dropping index column from header. <2012-04-20 Fri>

new Spectrum class v0.2.0 has tic slot. <2012-04-21 Sat>

tic method (data stored as a Spectrum slot) now returns total ion
current (as commonly used) and total ion count is obtain using
ionCount. <2012-04-21 Sat>

Fixed prune_taxa so that it properly fails with a message if the
taxa argument is a logical of wrong length. There was some
potential (and no warning) for unpredictable vector-recycling with
short vectors in the old implementation.

phyloseq 1.1.40

Huge Update and Renaming Event.

Made all functions use an underscore for English word delimiter,
if they were using an abbreviation.

Replaced “species” in all function names with “taxa”.

These changes are all backward compatible, for now, so your old code
should work. Let me know if it doesn’t and I will quickly make the
adjustment. This will remain true through the next official release,
but functional references to “species” will not be supported
afterward, except in the occasions where you actually mean taxonomic
species, like tax_glom(x, "species").

phyloseq 1.1.33

Revise taxglom() such that it handles phyloseq and taxonomyTable
classes, throws warning otherwise. It should not take a
manually-produced character vector, as this is roughly equivalent to
functionality supported in other method, especially
prune_species()/merge_species().

Also added unit-tests and executable examples for taxglom().

Got rid of taxglom.internal, incorporated directly into taxglom().
taxglom() is no longer an S4-method, and doesn’t need to be now that
the character-vector argument option is omitted, with S4-class
handling delegated to merge_species().

Add unit tests and example files for import_biom (as well as
import(“biom”,…) ).

phyloseq 1.1.28

Added rarefy_even_depth() function for random subsampling of
microbiome samples to the same number of reads. Default uses the
minimum total reads among the samples in the dataset. This is based
on the core “sample” function, which can have its random number
generator fixed by set.seed for reproducibility.

phyloseq 1.1.27

Fix bug in plot_ordination that caused an error rather than produce
unannotated plots when sampleData absent in the input.

phyloseq 1.1.23-26

Added unit tests and bugfixes

phyloseq 1.1.19-22

Improving import_qiime() importer to handle large datasets, like the
HMPv35 dataset, for example, while also providing useful status
messages during non-trivial imports that might take 10 minutes or
more to complete.

phyloseq 1.1.18

Added replicate labels as a “Sample” factor in the soilrep dataset.

phyloseq 1.1.17

Fix possible bug that results from the latest version (0.6+) of
igraph not being backward compatible. A stable igraph0 package is
available on CRAN as a stop-gap, and so all igraph dependencies were
migrated to “igraph0” until the phyloseq-source can be updated to
match the igraph latest.

ReactomePA

remove most of the codes in enrichPathway, instead import
enrich.internal in DOSE. implement some S3 method for mapping.
pathID2Name was rename to TERM2NAME, which will called by
enrich.internal. <2012-03-12, Tue>

RedeR

Version Date Category Text
2011-03-23

ReQON

Changes in version 1.3.4:

NEW FEATURES

An additional option has been added to ReadPosErrorPlot.R. The
option “startpos” now allows users to designate the starting read
position to be plotted. The default start position is 1.

BUG FIXES

The required version of R has increased to 2.15. Major changes to
the ReQON package were made in version 1.2.0, and older versions of R
would download ReQON v. 1.0.0, which is incompatible with the current
documentation.

There were minor inconsistencies between the FWSE reported in the
output plots and FWSE calculated from the recalibrated BAM file,
which has now been fixed.

Changes in version 1.3.2:

NEW FEATURES

ReQON no longer recalibrates ‘N’ bases. It returns the original
quality score for these bases in the output BAM file.

Minor modifications have been made to FWSEplot.R. Points are now
shaded according to the relative frequency of bases assigned that
quality score. See the reference manual for more details.

Rgraphviz

Changes in version 2.1:

Rgraphviz now requires Graphviz >= 2.16.

Added graphvizCapabilities() that reports the capabilities of
Graphviz. This requires Graphviz >= 2.28 and returns NULL if the
Rgraphviz installation does not support it.

Risa

This is the first release version of the package. It contains
functionality to parse ISAtab datasets into an R object from the
ISAtab class. It also provides functionality to save the ISA-tab
dataset, or each of its individual files. Additionally, it is also
possible to update assay files. Currently, metadata associated to
proteomics and metabolomics-based assays (i.e. mass spectrometry) can
be processed into an xcmsSet object (from the xcms R package

TargetSearch

Changes in version 1.14.0:

NEW FEATURES

New function ‘fixRI’. This function can be used to correct RI markers
or to manually force their location to specific retention times if,
for example, the RI markers were not co-injected with the biological
samples. Replaces the now deprecated ‘fixRIcorrection’.